Susilo et al., 2021 - Google Patents
Prognostics of induction motor shaft based on feature importance and least square support vector machine regressionSusilo et al., 2021
View PDF- Document ID
- 17230706998573825757
- Author
- Susilo D
- Widodo A
- Prahasto T
- Nizam M
- Publication year
- Publication venue
- International Journal of Automotive and Mechanical Engineering
External Links
Snippet
This paper aims to present a prognostic method for induction motor shafts that experience fatigue failure in the keyway area, using motor vibration signals. Preprocessing the data to eliminate noise in raw signals is done by decomposing the signal, using discrete wavelet …
- 230000001939 inductive effect 0 title abstract description 43
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ribeiro Junior et al. | Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks | |
| Zeng et al. | Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation | |
| Jin et al. | Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis–Taguchi system | |
| Liao et al. | Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model | |
| Kandukuri et al. | A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management | |
| Li et al. | Bearing fault feature selection method based on weighted multidimensional feature fusion | |
| Wang et al. | Weighted K-NN classification method of bearings fault diagnosis with multi-dimensional sensitive features | |
| Wang | Condition-based maintenance modelling | |
| Strączkiewicz et al. | Application of artificial neural network for damage detection in planetary gearbox of wind turbine | |
| CN103547976A (en) | Determination of damage and remaining useful life of rotating machinery including drive trains, gearboxes and generators | |
| Alzghoul et al. | On the Usefulness of Pre-processing Methods in Rotating Machines Faults Classification using Artificial Neural Network | |
| Kundu et al. | Gear pitting severity level identification using binary segmentation methodology | |
| CN107403279A (en) | A kind of adaptive status early warning system and method for oil transportation pump condition | |
| Mishra et al. | Performance evaluation of support vector machine for system level multi-fault diagnosis | |
| Yousaf et al. | Review of Advanced Approaches and Vibration Signature Analysis for Fault Detection, Diagnosis, and Prognosis of Rolling Element Bearings | |
| Susilo et al. | Prognostics of induction motor shaft based on feature importance and least square support vector machine regression | |
| Niknam et al. | Techniques of trend analysis in degradation-based prognostics | |
| Qi et al. | Sensorless robust anomaly detection of roller chain systems based on motor driver data and deep weighted KNN | |
| Mishra et al. | Hybrid models for rotating machinery diagnosis and prognosis: estimation of remaining useful life | |
| Miao et al. | Singularity detection in machinery health monitoring using Lipschitz exponent function | |
| Zhang et al. | Wear Calculation‐Based Degradation Analysis and Modeling for Remaining Useful Life Prediction of Ball Screw | |
| Sharp | Prognostic approaches using transient monitoring methods | |
| Salem et al. | Improved condition monitoring technique for wind turbine gearbox and shaft stress detection | |
| Abbasi et al. | Condition based maintenance of oil and gas equipment: A review | |
| Kumar et al. | A methodological integration of fisher score technique with intelligent machine learning methods for ball bearing fault investigation |